Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	21,180
2	Louisiana	20,435
3	New Jersey	20,145
4	Arizona	19,965
5	Rhode Island	16,900
6	Florida	16,779
7	Massachusetts	16,509
8	District of Columbia	16,066
9	Mississippi	14,746
10	Alabama	14,050
11	Delaware	13,991
12	South Carolina	13,876
13	Connecticut	13,478
14	Maryland	13,108
15	Illinois	12,959
16	Georgia	12,506
17	Iowa	12,469
18	Nevada	11,949
19	Texas	11,921
20	Nebraska	11,810
21	Tennessee	11,413
22	Arkansas	11,242
23	Utah	10,769
24	California	10,128
25	North Carolina	9,657
26	Virginia	9,182
27	South Dakota	8,978
28	Indiana	8,705
29	Idaho	8,606
30	Minnesota	8,359
31	Pennsylvania	8,318
32	Michigan	8,259
33	New Mexico	8,210
34	Kansas	8,081
35	Wisconsin	8,029
36	Colorado	7,058
37	North Dakota	6,731
38	Washington	6,559
39	Ohio	6,516
40	Oklahoma	6,427
41	Missouri	5,873
42	Kentucky	5,366
43	New Hampshire	4,595
44	Puerto Rico	3,901
45	Wyoming	3,778
46	Oregon	3,554
47	Alaska	3,297
48	West Virginia	2,869
49	Maine	2,760
50	Montana	2,470
51	Vermont	2,179
52	Hawaii	968

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	514
2	Louisiana	451
3	Alabama	394
4	Nevada	371
5	South Carolina	348
6	Mississippi	342
7	Arizona	308
8	Tennessee	286
9	Texas	280
10	Idaho	279
11	Georgia	267
12	Arkansas	239
13	California	213
14	Utah	203
15	North Carolina	176
16	Kansas	154
17	Iowa	152
18	Alaska	149
19	North Dakota	146
20	Wisconsin	143
21	Puerto Rico	139
22	Missouri	136
23	Kentucky	135
24	Maryland	127
25	Minnesota	123
26	Indiana	121
27	New Mexico	120
28	Washington	119
29	Virginia	114
30	Ohio	110
31	Oklahoma	108
32	District of Columbia	105
33	Delaware	98
34	Oregon	93
35	Illinois	87
36	Nebraska	83
37	Montana	80
38	Colorado	71
39	Wyoming	67
40	West Virginia	66
41	Pennsylvania	60
42	South Dakota	58
43	Michigan	57
44	Massachusetts	44
45	Rhode Island	34
46	New York	30
47	New Hampshire	20
48	Maine	18
49	Connecticut	15
50	New Jersey	15
51	Hawaii	13
52	Vermont	13

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,769
2	New York	1,655
3	Connecticut	1,235
4	Massachusetts	1,223
5	Rhode Island	939
6	District of Columbia	820
7	Louisiana	768
8	Michigan	638
9	Illinois	591
10	Maryland	559
11	Pennsylvania	552
12	Delaware	537
13	Mississippi	456
14	Indiana	419
15	Arizona	383
16	Colorado	305
17	Georgia	293
18	New Hampshire	292
19	Minnesota	281
20	New Mexico	275
21	Ohio	272
22	Alabama	263
23	Iowa	252
24	Virginia	237
25	Florida	236
26	South Carolina	226
27	Nevada	211
28	Washington	199
29	California	196
30	Missouri	189
31	Nebraska	161
32	North Carolina	159
33	Kentucky	155
34	Wisconsin	146
35	Texas	143
36	South Dakota	133
37	North Dakota	127
38	Tennessee	122
39	Arkansas	120
40	Oklahoma	114
41	Kansas	109
42	Vermont	89
43	Maine	87
44	Utah	77
45	Idaho	68
46	Oregon	62
47	Puerto Rico	56
48	West Virginia	55
49	Wyoming	41
50	Montana	36
51	Alaska	21
52	Hawaii	16

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	9
2	Florida	4
3	Louisiana	4
4	South Carolina	4
5	Texas	3
6	Mississippi	2
7	New Mexico	2
8	Ohio	2
9	Alabama	1
10	Arkansas	1
11	California	1
12	Georgia	1
13	Indiana	1
14	Iowa	1
15	Maryland	1
16	Massachusetts	1
17	Nevada	1
18	New Jersey	1
19	North Carolina	1
20	North Dakota	1
21	Rhode Island	1
22	Tennessee	1
23	Utah	1
24	Washington	1
25	Alaska	0
26	Colorado	0
27	Connecticut	0
28	Delaware	0
29	District of Columbia	0
30	Hawaii	0
31	Idaho	0
32	Illinois	0
33	Kansas	0
34	Kentucky	0
35	Maine	0
36	Michigan	0
37	Minnesota	0
38	Missouri	0
39	Montana	0
40	Nebraska	0
41	New Hampshire	0
42	New York	0
43	Oklahoma	0
44	Oregon	0
45	Pennsylvania	0
46	Puerto Rico	0
47	South Dakota	0
48	Vermont	0
49	Virginia	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	136,742	1	99
Lake	Tennessee	100,057	2	99
Lee	Arkansas	97,663	3	99
Dakota	Nebraska	93,528	4	99
Buena Vista	Iowa	89,551	5	99
Davidson	Tennessee	25,695	116	96
Richland	South Carolina	14,951	384	87
Orange	California	9,442	804	74
York	South Carolina	8,677	896	71
Pierce	Washington	4,721	1546	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,830	5	99
Richland	South Carolina	262	660	78
Davidson	Tennessee	239	706	77
Orange	California	155	964	69
Pierce	Washington	133	1072	65
York	South Carolina	50	1691	46

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.